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        <a href="deepbelief-module.html">Package&nbsp;deepbelief</a> ::
        Module&nbsp;abstractbm
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<h1 class="epydoc">Source Code for <a href="deepbelief.abstractbm-module.html">Module deepbelief.abstractbm</a></h1>
<pre class="py-src">
<a name="L1"></a><tt class="py-lineno">  1</tt>  <tt class="py-line"><tt class="py-keyword">import</tt> <tt class="py-name">numpy</tt> <tt class="py-keyword">as</tt> <tt class="py-name">np</tt> </tt>
<a name="L2"></a><tt class="py-lineno">  2</tt>  <tt class="py-line"> </tt>
<a name="L3"></a><tt class="py-lineno">  3</tt>  <tt class="py-line"><tt class="py-name">__license__</tt> <tt class="py-op">=</tt> <tt class="py-string">'MIT License &lt;http://www.opensource.org/licenses/mit-license.php&gt;'</tt> </tt>
<a name="L4"></a><tt class="py-lineno">  4</tt>  <tt class="py-line"><tt class="py-name">__author__</tt> <tt class="py-op">=</tt> <tt class="py-string">'Lucas Theis &lt;lucas@tuebingen.mpg.de&gt;'</tt> </tt>
<a name="L5"></a><tt class="py-lineno">  5</tt>  <tt class="py-line"><tt class="py-name">__docformat__</tt> <tt class="py-op">=</tt> <tt class="py-string">'epytext'</tt> </tt>
<a name="L6"></a><tt class="py-lineno">  6</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM"></a><div id="AbstractBM-def"><a name="L7"></a><tt class="py-lineno">  7</tt> <a class="py-toggle" href="#" id="AbstractBM-toggle" onclick="return toggle('AbstractBM');">-</a><tt class="py-line"><tt class="py-keyword">class</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html">AbstractBM</a><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM-collapsed" style="display:none;" pad="+++" indent="++++"></div><div id="AbstractBM-expanded"><a name="L8"></a><tt class="py-lineno">  8</tt>  <tt class="py-line">        <tt class="py-docstring">"""</tt> </tt>
<a name="L9"></a><tt class="py-lineno">  9</tt>  <tt class="py-line"><tt class="py-docstring">        Provides an interface and common functionality for latent-variable</tt> </tt>
<a name="L10"></a><tt class="py-lineno"> 10</tt>  <tt class="py-line"><tt class="py-docstring">        Boltzmann machines, such as contrastive divergence learning, Gibbs</tt> </tt>
<a name="L11"></a><tt class="py-lineno"> 11</tt>  <tt class="py-line"><tt class="py-docstring">        sampling and hybrid Monte Carlo sampling.</tt> </tt>
<a name="L12"></a><tt class="py-lineno"> 12</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L13"></a><tt class="py-lineno"> 13</tt>  <tt class="py-line"><tt class="py-docstring">        B{References:}</tt> </tt>
<a name="L14"></a><tt class="py-lineno"> 14</tt>  <tt class="py-line"><tt class="py-docstring">                - Hinton, G. E. (2002). I{Training Products of Experts by Minimizing</tt> </tt>
<a name="L15"></a><tt class="py-lineno"> 15</tt>  <tt class="py-line"><tt class="py-docstring">                Contrastive Divergence.} Neural Computation.</tt> </tt>
<a name="L16"></a><tt class="py-lineno"> 16</tt>  <tt class="py-line"><tt class="py-docstring">                - Neal, R. (1996). I{Bayesian Learning for Neural Networks.} Springer Verlag.</tt> </tt>
<a name="L17"></a><tt class="py-lineno"> 17</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L18"></a><tt class="py-lineno"> 18</tt>  <tt class="py-line"><tt class="py-docstring">        @type X: matrix</tt> </tt>
<a name="L19"></a><tt class="py-lineno"> 19</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar X: states of the visible units</tt> </tt>
<a name="L20"></a><tt class="py-lineno"> 20</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L21"></a><tt class="py-lineno"> 21</tt>  <tt class="py-line"><tt class="py-docstring">        @type Y: matrix</tt> </tt>
<a name="L22"></a><tt class="py-lineno"> 22</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar Y: states of the hidden units</tt> </tt>
<a name="L23"></a><tt class="py-lineno"> 23</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L24"></a><tt class="py-lineno"> 24</tt>  <tt class="py-line"><tt class="py-docstring">        @type W: matrix</tt> </tt>
<a name="L25"></a><tt class="py-lineno"> 25</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar W: weight matrix connecting visible and hidden units</tt> </tt>
<a name="L26"></a><tt class="py-lineno"> 26</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L27"></a><tt class="py-lineno"> 27</tt>  <tt class="py-line"><tt class="py-docstring">        @type b: matrix</tt> </tt>
<a name="L28"></a><tt class="py-lineno"> 28</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar b: visible biases</tt> </tt>
<a name="L29"></a><tt class="py-lineno"> 29</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L30"></a><tt class="py-lineno"> 30</tt>  <tt class="py-line"><tt class="py-docstring">        @type c: matrix</tt> </tt>
<a name="L31"></a><tt class="py-lineno"> 31</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar c: hidden biases</tt> </tt>
<a name="L32"></a><tt class="py-lineno"> 32</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L33"></a><tt class="py-lineno"> 33</tt>  <tt class="py-line"><tt class="py-docstring">        @type learning_rate: real</tt> </tt>
<a name="L34"></a><tt class="py-lineno"> 34</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar learning_rate: step width of gradient descent learning algorithm</tt> </tt>
<a name="L35"></a><tt class="py-lineno"> 35</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L36"></a><tt class="py-lineno"> 36</tt>  <tt class="py-line"><tt class="py-docstring">        @type momentum: real</tt> </tt>
<a name="L37"></a><tt class="py-lineno"> 37</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar momentum: parameter of the learning algorithm</tt> </tt>
<a name="L38"></a><tt class="py-lineno"> 38</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L39"></a><tt class="py-lineno"> 39</tt>  <tt class="py-line"><tt class="py-docstring">        @type weight_decay: real</tt> </tt>
<a name="L40"></a><tt class="py-lineno"> 40</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar weight_decay: prevents the weights from becoming too large</tt> </tt>
<a name="L41"></a><tt class="py-lineno"> 41</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L42"></a><tt class="py-lineno"> 42</tt>  <tt class="py-line"><tt class="py-docstring">        @type sparseness: real</tt> </tt>
<a name="L43"></a><tt class="py-lineno"> 43</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar sparseness: encourage sparse activation of the hidden units by</tt> </tt>
<a name="L44"></a><tt class="py-lineno"> 44</tt>  <tt class="py-line"><tt class="py-docstring">        modifying the biases</tt> </tt>
<a name="L45"></a><tt class="py-lineno"> 45</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L46"></a><tt class="py-lineno"> 46</tt>  <tt class="py-line"><tt class="py-docstring">        @type sparseness_target: real</tt> </tt>
<a name="L47"></a><tt class="py-lineno"> 47</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar sparseness_target: targeted level of activity</tt> </tt>
<a name="L48"></a><tt class="py-lineno"> 48</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L49"></a><tt class="py-lineno"> 49</tt>  <tt class="py-line"><tt class="py-docstring">        @type cd_steps: integer</tt> </tt>
<a name="L50"></a><tt class="py-lineno"> 50</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar cd_steps: number of Gibbs updates to approximate learning gradient</tt> </tt>
<a name="L51"></a><tt class="py-lineno"> 51</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L52"></a><tt class="py-lineno"> 52</tt>  <tt class="py-line"><tt class="py-docstring">        @type persistent: boolean</tt> </tt>
<a name="L53"></a><tt class="py-lineno"> 53</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar persistent: use persistent Markov chains to approximate learning gradient</tt> </tt>
<a name="L54"></a><tt class="py-lineno"> 54</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L55"></a><tt class="py-lineno"> 55</tt>  <tt class="py-line"><tt class="py-docstring">        @type sampling_method: integer</tt> </tt>
<a name="L56"></a><tt class="py-lineno"> 56</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar sampling_method: method for drawing samples (typically L{GIBBS})</tt> </tt>
<a name="L57"></a><tt class="py-lineno"> 57</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L58"></a><tt class="py-lineno"> 58</tt>  <tt class="py-line"><tt class="py-docstring">        @type lf_steps: integer</tt> </tt>
<a name="L59"></a><tt class="py-lineno"> 59</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar lf_steps: number of I{leapfrog} steps in L{HMC} sampling</tt> </tt>
<a name="L60"></a><tt class="py-lineno"> 60</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L61"></a><tt class="py-lineno"> 61</tt>  <tt class="py-line"><tt class="py-docstring">        @type lf_step_size: real</tt> </tt>
<a name="L62"></a><tt class="py-lineno"> 62</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar lf_step_size: size of one leapfrog step</tt> </tt>
<a name="L63"></a><tt class="py-lineno"> 63</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L64"></a><tt class="py-lineno"> 64</tt>  <tt class="py-line"><tt class="py-docstring">        @type lf_adaptive: boolean</tt> </tt>
<a name="L65"></a><tt class="py-lineno"> 65</tt>  <tt class="py-line"><tt class="py-docstring">        @ivar lf_adaptive: automatically adjust C{lf_step_size}</tt> </tt>
<a name="L66"></a><tt class="py-lineno"> 66</tt>  <tt class="py-line"><tt class="py-docstring">        """</tt> </tt>
<a name="L67"></a><tt class="py-lineno"> 67</tt>  <tt class="py-line"> </tt>
<a name="L68"></a><tt class="py-lineno"> 68</tt>  <tt class="py-line">        <tt class="py-comment"># sampling type constants</tt> </tt>
<a name="L69"></a><tt class="py-lineno"> 69</tt>  <tt class="py-line">        <tt id="link-0" class="py-name" targets="Variable deepbelief.abstractbm.AbstractBM.GIBBS=deepbelief.abstractbm.AbstractBM-class.html#GIBBS"><a title="deepbelief.abstractbm.AbstractBM.GIBBS" class="py-name" href="#" onclick="return doclink('link-0', 'GIBBS', 'link-0');">GIBBS</a></tt><tt class="py-op">,</tt> <tt id="link-1" class="py-name" targets="Variable deepbelief.abstractbm.AbstractBM.HMC=deepbelief.abstractbm.AbstractBM-class.html#HMC"><a title="deepbelief.abstractbm.AbstractBM.HMC" class="py-name" href="#" onclick="return doclink('link-1', 'HMC', 'link-1');">HMC</a></tt><tt class="py-op">,</tt> <tt id="link-2" class="py-name" targets="Variable deepbelief.abstractbm.AbstractBM.MF=deepbelief.abstractbm.AbstractBM-class.html#MF"><a title="deepbelief.abstractbm.AbstractBM.MF" class="py-name" href="#" onclick="return doclink('link-2', 'MF', 'link-2');">MF</a></tt> <tt class="py-op">=</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-number">3</tt><tt class="py-op">)</tt> </tt>
<a name="L70"></a><tt class="py-lineno"> 70</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.__init__"></a><div id="AbstractBM.__init__-def"><a name="L71"></a><tt class="py-lineno"> 71</tt> <a class="py-toggle" href="#" id="AbstractBM.__init__-toggle" onclick="return toggle('AbstractBM.__init__');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#__init__">__init__</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">num_visibles</tt><tt class="py-op">,</tt> <tt class="py-param">num_hiddens</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.__init__-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.__init__-expanded"><a name="L72"></a><tt class="py-lineno"> 72</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L73"></a><tt class="py-lineno"> 73</tt>  <tt class="py-line"><tt class="py-docstring">                Initializes common parameters of Boltzmann machines.</tt> </tt>
<a name="L74"></a><tt class="py-lineno"> 74</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L75"></a><tt class="py-lineno"> 75</tt>  <tt class="py-line"><tt class="py-docstring">                @type  num_visibles: integer</tt> </tt>
<a name="L76"></a><tt class="py-lineno"> 76</tt>  <tt class="py-line"><tt class="py-docstring">                @param num_visibles: number of visible units</tt> </tt>
<a name="L77"></a><tt class="py-lineno"> 77</tt>  <tt class="py-line"><tt class="py-docstring">                @type  num_hiddens:  integer</tt> </tt>
<a name="L78"></a><tt class="py-lineno"> 78</tt>  <tt class="py-line"><tt class="py-docstring">                @param num_hiddens:  number of hidden units</tt> </tt>
<a name="L79"></a><tt class="py-lineno"> 79</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L80"></a><tt class="py-lineno"> 80</tt>  <tt class="py-line"> </tt>
<a name="L81"></a><tt class="py-lineno"> 81</tt>  <tt class="py-line">                <tt class="py-comment"># hyperparameters</tt> </tt>
<a name="L82"></a><tt class="py-lineno"> 82</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">learning_rate</tt> <tt class="py-op">=</tt> <tt class="py-number">0.01</tt> </tt>
<a name="L83"></a><tt class="py-lineno"> 83</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">weight_decay</tt> <tt class="py-op">=</tt> <tt class="py-number">0.001</tt> </tt>
<a name="L84"></a><tt class="py-lineno"> 84</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">momentum</tt> <tt class="py-op">=</tt> <tt class="py-number">0.5</tt> </tt>
<a name="L85"></a><tt class="py-lineno"> 85</tt>  <tt class="py-line"> </tt>
<a name="L86"></a><tt class="py-lineno"> 86</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">cd_steps</tt> <tt class="py-op">=</tt> <tt class="py-number">1</tt> </tt>
<a name="L87"></a><tt class="py-lineno"> 87</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">persistent</tt> <tt class="py-op">=</tt> <tt class="py-name">False</tt> </tt>
<a name="L88"></a><tt class="py-lineno"> 88</tt>  <tt class="py-line"> </tt>
<a name="L89"></a><tt class="py-lineno"> 89</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">sparseness</tt> <tt class="py-op">=</tt> <tt class="py-number">0.0</tt> </tt>
<a name="L90"></a><tt class="py-lineno"> 90</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">sparseness_target</tt> <tt class="py-op">=</tt> <tt class="py-number">0.1</tt> </tt>
<a name="L91"></a><tt class="py-lineno"> 91</tt>  <tt class="py-line"> </tt>
<a name="L92"></a><tt class="py-lineno"> 92</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">sampling_method</tt> <tt class="py-op">=</tt> <tt id="link-3" class="py-name" targets="Class deepbelief.abstractbm.AbstractBM=deepbelief.abstractbm.AbstractBM-class.html"><a title="deepbelief.abstractbm.AbstractBM" class="py-name" href="#" onclick="return doclink('link-3', 'AbstractBM', 'link-3');">AbstractBM</a></tt><tt class="py-op">.</tt><tt id="link-4" class="py-name"><a title="deepbelief.abstractbm.AbstractBM.GIBBS" class="py-name" href="#" onclick="return doclink('link-4', 'GIBBS', 'link-0');">GIBBS</a></tt> </tt>
<a name="L93"></a><tt class="py-lineno"> 93</tt>  <tt class="py-line"> </tt>
<a name="L94"></a><tt class="py-lineno"> 94</tt>  <tt class="py-line">                <tt class="py-comment"># relevant for HMC sampling</tt> </tt>
<a name="L95"></a><tt class="py-lineno"> 95</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">lf_steps</tt> <tt class="py-op">=</tt> <tt class="py-number">10</tt> </tt>
<a name="L96"></a><tt class="py-lineno"> 96</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">lf_step_size</tt> <tt class="py-op">=</tt> <tt class="py-number">0.01</tt> </tt>
<a name="L97"></a><tt class="py-lineno"> 97</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">lf_adaptive</tt> <tt class="py-op">=</tt> <tt class="py-name">True</tt> </tt>
<a name="L98"></a><tt class="py-lineno"> 98</tt>  <tt class="py-line"> </tt>
<a name="L99"></a><tt class="py-lineno"> 99</tt>  <tt class="py-line">                <tt class="py-comment"># parameters</tt> </tt>
<a name="L100"></a><tt class="py-lineno">100</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">W</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">random</tt><tt class="py-op">.</tt><tt class="py-name">randn</tt><tt class="py-op">(</tt><tt class="py-name">num_visibles</tt><tt class="py-op">,</tt> <tt class="py-name">num_hiddens</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> <tt class="py-op">/</tt> <tt class="py-op">(</tt><tt class="py-name">num_visibles</tt> <tt class="py-op">+</tt> <tt class="py-name">num_hiddens</tt><tt class="py-op">)</tt> </tt>
<a name="L101"></a><tt class="py-lineno">101</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">b</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">num_visibles</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> </tt>
<a name="L102"></a><tt class="py-lineno">102</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">c</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">num_hiddens</tt><tt class="py-op">)</tt> <tt class="py-op">-</tt> <tt class="py-number">1.</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> </tt>
<a name="L103"></a><tt class="py-lineno">103</tt>  <tt class="py-line"> </tt>
<a name="L104"></a><tt class="py-lineno">104</tt>  <tt class="py-line">                <tt class="py-comment"># increments</tt> </tt>
<a name="L105"></a><tt class="py-lineno">105</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dW</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">W</tt><tt class="py-op">)</tt> </tt>
<a name="L106"></a><tt class="py-lineno">106</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">db</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">b</tt><tt class="py-op">)</tt> </tt>
<a name="L107"></a><tt class="py-lineno">107</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dc</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">c</tt><tt class="py-op">)</tt> </tt>
<a name="L108"></a><tt class="py-lineno">108</tt>  <tt class="py-line"> </tt>
<a name="L109"></a><tt class="py-lineno">109</tt>  <tt class="py-line">                <tt class="py-comment"># variables</tt> </tt>
<a name="L110"></a><tt class="py-lineno">110</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">num_visibles</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> </tt>
<a name="L111"></a><tt class="py-lineno">111</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">num_hiddens</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> </tt>
<a name="L112"></a><tt class="py-lineno">112</tt>  <tt class="py-line"> </tt>
<a name="L113"></a><tt class="py-lineno">113</tt>  <tt class="py-line">                <tt class="py-comment"># probabilities</tt> </tt>
<a name="L114"></a><tt class="py-lineno">114</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">P</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">)</tt> </tt>
<a name="L115"></a><tt class="py-lineno">115</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Q</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt><tt class="py-op">)</tt> </tt>
<a name="L116"></a><tt class="py-lineno">116</tt>  <tt class="py-line"> </tt>
<a name="L117"></a><tt class="py-lineno">117</tt>  <tt class="py-line">                <tt class="py-comment"># states of persistent Markov chain</tt> </tt>
<a name="L118"></a><tt class="py-lineno">118</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pX</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt class="py-name">num_visibles</tt><tt class="py-op">,</tt> <tt class="py-number">100</tt><tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
<a name="L119"></a><tt class="py-lineno">119</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pY</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt class="py-name">num_hiddens</tt><tt class="py-op">,</tt> <tt class="py-number">100</tt><tt class="py-op">]</tt><tt class="py-op">)</tt> </tt>
<a name="L120"></a><tt class="py-lineno">120</tt>  <tt class="py-line"> </tt>
<a name="L121"></a><tt class="py-lineno">121</tt>  <tt class="py-line">                <tt class="py-comment"># used by annealed importance sampling</tt> </tt>
<a name="L122"></a><tt class="py-lineno">122</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ais_logz</tt> <tt class="py-op">=</tt> <tt class="py-name">None</tt> </tt>
<a name="L123"></a><tt class="py-lineno">123</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ais_samples</tt> <tt class="py-op">=</tt> <tt class="py-name">None</tt> </tt>
<a name="L124"></a><tt class="py-lineno">124</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">ais_logweights</tt> <tt class="py-op">=</tt> <tt class="py-name">None</tt> </tt>
</div><a name="L125"></a><tt class="py-lineno">125</tt>  <tt class="py-line"> </tt>
<a name="L126"></a><tt class="py-lineno">126</tt>  <tt class="py-line"> </tt>
<a name="L127"></a><tt class="py-lineno">127</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.forward"></a><div id="AbstractBM.forward-def"><a name="L128"></a><tt class="py-lineno">128</tt> <a class="py-toggle" href="#" id="AbstractBM.forward-toggle" onclick="return toggle('AbstractBM.forward');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#forward">forward</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.forward-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.forward-expanded"><a name="L129"></a><tt class="py-lineno">129</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L130"></a><tt class="py-lineno">130</tt>  <tt class="py-line"><tt class="py-docstring">                Conditionally samples the hidden units. If no input is given, the current</tt> </tt>
<a name="L131"></a><tt class="py-lineno">131</tt>  <tt class="py-line"><tt class="py-docstring">                state of the visible units is used.</tt> </tt>
<a name="L132"></a><tt class="py-lineno">132</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L133"></a><tt class="py-lineno">133</tt>  <tt class="py-line"><tt class="py-docstring">                @type  X: array_like</tt> </tt>
<a name="L134"></a><tt class="py-lineno">134</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: states of visible units</tt> </tt>
<a name="L135"></a><tt class="py-lineno">135</tt>  <tt class="py-line"><tt class="py-docstring">                @rtype:   matrix</tt> </tt>
<a name="L136"></a><tt class="py-lineno">136</tt>  <tt class="py-line"><tt class="py-docstring">                @return:  a matrix containing states for the hidden units</tt> </tt>
<a name="L137"></a><tt class="py-lineno">137</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L138"></a><tt class="py-lineno">138</tt>  <tt class="py-line"> </tt>
<a name="L139"></a><tt class="py-lineno">139</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'forward\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L140"></a><tt class="py-lineno">140</tt>  <tt class="py-line"> </tt>
<a name="L141"></a><tt class="py-lineno">141</tt>  <tt class="py-line"> </tt>
<a name="L142"></a><tt class="py-lineno">142</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.backward"></a><div id="AbstractBM.backward-def"><a name="L143"></a><tt class="py-lineno">143</tt> <a class="py-toggle" href="#" id="AbstractBM.backward-toggle" onclick="return toggle('AbstractBM.backward');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#backward">backward</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.backward-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.backward-expanded"><a name="L144"></a><tt class="py-lineno">144</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L145"></a><tt class="py-lineno">145</tt>  <tt class="py-line"><tt class="py-docstring">                Conditionally samples the visible units. If C{Y} or C{X} is given, the</tt> </tt>
<a name="L146"></a><tt class="py-lineno">146</tt>  <tt class="py-line"><tt class="py-docstring">                state of the Boltzmann machine is changed prior to sampling.</tt> </tt>
<a name="L147"></a><tt class="py-lineno">147</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L148"></a><tt class="py-lineno">148</tt>  <tt class="py-line"><tt class="py-docstring">                @type Y:  array_like</tt> </tt>
<a name="L149"></a><tt class="py-lineno">149</tt>  <tt class="py-line"><tt class="py-docstring">                @param Y: states of hidden units</tt> </tt>
<a name="L150"></a><tt class="py-lineno">150</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L151"></a><tt class="py-lineno">151</tt>  <tt class="py-line"><tt class="py-docstring">                @type X:  array_like</tt> </tt>
<a name="L152"></a><tt class="py-lineno">152</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: states of visible units</tt> </tt>
<a name="L153"></a><tt class="py-lineno">153</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L154"></a><tt class="py-lineno">154</tt>  <tt class="py-line"><tt class="py-docstring">                @rtype:  matrix</tt> </tt>
<a name="L155"></a><tt class="py-lineno">155</tt>  <tt class="py-line"><tt class="py-docstring">                @return: a matrix containing states for the visible units</tt> </tt>
<a name="L156"></a><tt class="py-lineno">156</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L157"></a><tt class="py-lineno">157</tt>  <tt class="py-line"> </tt>
<a name="L158"></a><tt class="py-lineno">158</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'backward\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L159"></a><tt class="py-lineno">159</tt>  <tt class="py-line"> </tt>
<a name="L160"></a><tt class="py-lineno">160</tt>  <tt class="py-line"> </tt>
<a name="L161"></a><tt class="py-lineno">161</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.sample"></a><div id="AbstractBM.sample-def"><a name="L162"></a><tt class="py-lineno">162</tt> <a class="py-toggle" href="#" id="AbstractBM.sample-toggle" onclick="return toggle('AbstractBM.sample');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#sample">sample</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">num_samples</tt><tt class="py-op">=</tt><tt class="py-number">1</tt><tt class="py-op">,</tt> <tt class="py-param">burn_in_length</tt><tt class="py-op">=</tt><tt class="py-number">100</tt><tt class="py-op">,</tt> <tt class="py-param">sample_spacing</tt><tt class="py-op">=</tt><tt class="py-number">20</tt><tt class="py-op">,</tt> <tt class="py-param">num_parallel_chains</tt><tt class="py-op">=</tt><tt class="py-number">1</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">=</tt><tt class="py-name">None</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.sample-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.sample-expanded"><a name="L163"></a><tt class="py-lineno">163</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L164"></a><tt class="py-lineno">164</tt>  <tt class="py-line"><tt class="py-docstring">                Draws samples from the model using Gibbs or hybrid Monte Carlo sampling.</tt> </tt>
<a name="L165"></a><tt class="py-lineno">165</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L166"></a><tt class="py-lineno">166</tt>  <tt class="py-line"><tt class="py-docstring">                @type  num_samples: integer</tt> </tt>
<a name="L167"></a><tt class="py-lineno">167</tt>  <tt class="py-line"><tt class="py-docstring">                @param num_samples: the number of samples to draw from the model</tt> </tt>
<a name="L168"></a><tt class="py-lineno">168</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L169"></a><tt class="py-lineno">169</tt>  <tt class="py-line"><tt class="py-docstring">                @type  burn_in_length: integer</tt> </tt>
<a name="L170"></a><tt class="py-lineno">170</tt>  <tt class="py-line"><tt class="py-docstring">                @param burn_in_length: the number of discarded initial samples</tt> </tt>
<a name="L171"></a><tt class="py-lineno">171</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L172"></a><tt class="py-lineno">172</tt>  <tt class="py-line"><tt class="py-docstring">                @type  sample_spacing: integer</tt> </tt>
<a name="L173"></a><tt class="py-lineno">173</tt>  <tt class="py-line"><tt class="py-docstring">                @param sample_spacing: return only every I{n}-th sample of the Markov chain</tt> </tt>
<a name="L174"></a><tt class="py-lineno">174</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L175"></a><tt class="py-lineno">175</tt>  <tt class="py-line"><tt class="py-docstring">                @type  num_parallel_chains: integer</tt> </tt>
<a name="L176"></a><tt class="py-lineno">176</tt>  <tt class="py-line"><tt class="py-docstring">                @param num_parallel_chains: number of parallel Markov chains</tt> </tt>
<a name="L177"></a><tt class="py-lineno">177</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L178"></a><tt class="py-lineno">178</tt>  <tt class="py-line"><tt class="py-docstring">                @type  X: array_like</tt> </tt>
<a name="L179"></a><tt class="py-lineno">179</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: initial state(s) of Markov chain(s)</tt> </tt>
<a name="L180"></a><tt class="py-lineno">180</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L181"></a><tt class="py-lineno">181</tt>  <tt class="py-line"><tt class="py-docstring">                @rtype:  matrix</tt> </tt>
<a name="L182"></a><tt class="py-lineno">182</tt>  <tt class="py-line"><tt class="py-docstring">                @return: a matrix containing the drawn samples in its columns</tt> </tt>
<a name="L183"></a><tt class="py-lineno">183</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L184"></a><tt class="py-lineno">184</tt>  <tt class="py-line"> </tt>
<a name="L185"></a><tt class="py-lineno">185</tt>  <tt class="py-line">                <tt class="py-comment"># preparations</tt> </tt>
<a name="L186"></a><tt class="py-lineno">186</tt>  <tt class="py-line">                <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">persistent</tt> <tt class="py-keyword">and</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pX</tt><tt class="py-op">.</tt><tt class="py-name">shape</tt><tt class="py-op">[</tt><tt class="py-number">1</tt><tt class="py-op">]</tt> <tt class="py-op">==</tt> <tt class="py-name">num_parallel_chains</tt><tt class="py-op">:</tt> </tt>
<a name="L187"></a><tt class="py-lineno">187</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pX</tt> </tt>
<a name="L188"></a><tt class="py-lineno">188</tt>  <tt class="py-line">                <tt class="py-keyword">else</tt><tt class="py-op">:</tt> </tt>
<a name="L189"></a><tt class="py-lineno">189</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-op">[</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">shape</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">,</tt> <tt class="py-name">num_parallel_chains</tt><tt class="py-op">]</tt><tt class="py-op">)</tt> <tt class="py-keyword">if</tt> <tt class="py-name">X</tt> <tt class="py-keyword">is</tt> <tt class="py-name">None</tt> <tt class="py-keyword">else</tt> <tt class="py-name">X</tt> </tt>
<a name="L190"></a><tt class="py-lineno">190</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">)</tt> </tt>
<a name="L191"></a><tt class="py-lineno">191</tt>  <tt class="py-line"> </tt>
<a name="L192"></a><tt class="py-lineno">192</tt>  <tt class="py-line">                <tt class="py-name">sample_step</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_sample_hmc_step</tt> <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">sampling_method</tt> <tt class="py-keyword">is</tt> <tt id="link-5" class="py-name"><a title="deepbelief.abstractbm.AbstractBM" class="py-name" href="#" onclick="return doclink('link-5', 'AbstractBM', 'link-3');">AbstractBM</a></tt><tt class="py-op">.</tt><tt id="link-6" class="py-name"><a title="deepbelief.abstractbm.AbstractBM.HMC" class="py-name" href="#" onclick="return doclink('link-6', 'HMC', 'link-1');">HMC</a></tt> <tt class="py-keyword">else</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_sample_gibbs_step</tt> </tt>
<a name="L193"></a><tt class="py-lineno">193</tt>  <tt class="py-line">                <tt class="py-name">samples</tt> <tt class="py-op">=</tt> <tt class="py-op">[</tt><tt class="py-op">]</tt> </tt>
<a name="L194"></a><tt class="py-lineno">194</tt>  <tt class="py-line"> </tt>
<a name="L195"></a><tt class="py-lineno">195</tt>  <tt class="py-line">                <tt class="py-comment"># burn-in phase</tt> </tt>
<a name="L196"></a><tt class="py-lineno">196</tt>  <tt class="py-line">                <tt class="py-keyword">for</tt> <tt class="py-name">t</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">burn_in_length</tt> <tt class="py-op">-</tt> <tt class="py-name">sample_spacing</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L197"></a><tt class="py-lineno">197</tt>  <tt class="py-line">                        <tt class="py-name">sample_step</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L198"></a><tt class="py-lineno">198</tt>  <tt class="py-line"> </tt>
<a name="L199"></a><tt class="py-lineno">199</tt>  <tt class="py-line">                <tt class="py-keyword">for</tt> <tt class="py-name">s</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">int</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">ceil</tt><tt class="py-op">(</tt><tt class="py-name">num_samples</tt> <tt class="py-op">/</tt> <tt class="py-name">float</tt><tt class="py-op">(</tt><tt class="py-name">num_parallel_chains</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L200"></a><tt class="py-lineno">200</tt>  <tt class="py-line">                        <tt class="py-keyword">for</tt> <tt class="py-name">t</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">sample_spacing</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L201"></a><tt class="py-lineno">201</tt>  <tt class="py-line">                                <tt class="py-name">sample_step</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L202"></a><tt class="py-lineno">202</tt>  <tt class="py-line">                        <tt class="py-name">samples</tt><tt class="py-op">.</tt><tt class="py-name">append</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">copy</tt><tt class="py-op">(</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
<a name="L203"></a><tt class="py-lineno">203</tt>  <tt class="py-line"> </tt>
<a name="L204"></a><tt class="py-lineno">204</tt>  <tt class="py-line">                <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">persistent</tt><tt class="py-op">:</tt> </tt>
<a name="L205"></a><tt class="py-lineno">205</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pX</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">copy</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L206"></a><tt class="py-lineno">206</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pY</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt><tt class="py-op">.</tt><tt class="py-name">copy</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L207"></a><tt class="py-lineno">207</tt>  <tt class="py-line"> </tt>
<a name="L208"></a><tt class="py-lineno">208</tt>  <tt class="py-line">                <tt class="py-keyword">return</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">concatenate</tt><tt class="py-op">(</tt><tt class="py-name">samples</tt><tt class="py-op">,</tt> <tt class="py-number">1</tt><tt class="py-op">)</tt><tt class="py-op">[</tt><tt class="py-op">:</tt><tt class="py-op">,</tt> <tt class="py-op">:</tt><tt class="py-name">num_samples</tt><tt class="py-op">]</tt> </tt>
</div><a name="L209"></a><tt class="py-lineno">209</tt>  <tt class="py-line"> </tt>
<a name="L210"></a><tt class="py-lineno">210</tt>  <tt class="py-line"> </tt>
<a name="L211"></a><tt class="py-lineno">211</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.train"></a><div id="AbstractBM.train-def"><a name="L212"></a><tt class="py-lineno">212</tt> <a class="py-toggle" href="#" id="AbstractBM.train-toggle" onclick="return toggle('AbstractBM.train');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#train">train</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.train-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.train-expanded"><a name="L213"></a><tt class="py-lineno">213</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L214"></a><tt class="py-lineno">214</tt>  <tt class="py-line"><tt class="py-docstring">                Trains the parameters of the BM on a batch of data samples. The</tt> </tt>
<a name="L215"></a><tt class="py-lineno">215</tt>  <tt class="py-line"><tt class="py-docstring">                data stored in C{X} is used to estimate the likelihood gradient and</tt> </tt>
<a name="L216"></a><tt class="py-lineno">216</tt>  <tt class="py-line"><tt class="py-docstring">                one step of gradient ascend is performed.</tt> </tt>
<a name="L217"></a><tt class="py-lineno">217</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L218"></a><tt class="py-lineno">218</tt>  <tt class="py-line"><tt class="py-docstring">                @type  X: array_like</tt> </tt>
<a name="L219"></a><tt class="py-lineno">219</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: example states of the visible units</tt> </tt>
<a name="L220"></a><tt class="py-lineno">220</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L221"></a><tt class="py-lineno">221</tt>  <tt class="py-line"> </tt>
<a name="L222"></a><tt class="py-lineno">222</tt>  <tt class="py-line">                <tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">X</tt><tt class="py-op">)</tt> </tt>
<a name="L223"></a><tt class="py-lineno">223</tt>  <tt class="py-line"> </tt>
<a name="L224"></a><tt class="py-lineno">224</tt>  <tt class="py-line">                <tt class="py-comment"># positive phase</tt> </tt>
<a name="L225"></a><tt class="py-lineno">225</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-7" class="py-name" targets="Method deepbelief.abstractbm.AbstractBM.forward()=deepbelief.abstractbm.AbstractBM-class.html#forward,Method deepbelief.dbn.DBN.forward()=deepbelief.dbn.DBN-class.html#forward,Method deepbelief.gaussianrbm.GaussianRBM.forward()=deepbelief.gaussianrbm.GaussianRBM-class.html#forward,Method deepbelief.mixbm.MixBM.forward()=deepbelief.mixbm.MixBM-class.html#forward,Method deepbelief.rbm.RBM.forward()=deepbelief.rbm.RBM-class.html#forward,Method deepbelief.semirbm.SemiRBM.forward()=deepbelief.semirbm.SemiRBM-class.html#forward"><a title="deepbelief.abstractbm.AbstractBM.forward
deepbelief.dbn.DBN.forward
deepbelief.gaussianrbm.GaussianRBM.forward
deepbelief.mixbm.MixBM.forward
deepbelief.rbm.RBM.forward
deepbelief.semirbm.SemiRBM.forward" class="py-name" href="#" onclick="return doclink('link-7', 'forward', 'link-7');">forward</a></tt><tt class="py-op">(</tt><tt class="py-name">X</tt><tt class="py-op">)</tt> </tt>
<a name="L226"></a><tt class="py-lineno">226</tt>  <tt class="py-line"> </tt>
<a name="L227"></a><tt class="py-lineno">227</tt>  <tt class="py-line">                <tt class="py-comment"># store posterior probabilities</tt> </tt>
<a name="L228"></a><tt class="py-lineno">228</tt>  <tt class="py-line">                <tt class="py-name">Q</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Q</tt><tt class="py-op">.</tt><tt class="py-name">copy</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L229"></a><tt class="py-lineno">229</tt>  <tt class="py-line"> </tt>
<a name="L230"></a><tt class="py-lineno">230</tt>  <tt class="py-line">                <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">persistent</tt><tt class="py-op">:</tt> </tt>
<a name="L231"></a><tt class="py-lineno">231</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pX</tt> </tt>
<a name="L232"></a><tt class="py-lineno">232</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pY</tt> </tt>
<a name="L233"></a><tt class="py-lineno">233</tt>  <tt class="py-line"> </tt>
<a name="L234"></a><tt class="py-lineno">234</tt>  <tt class="py-line">                <tt class="py-comment"># negative phase</tt> </tt>
<a name="L235"></a><tt class="py-lineno">235</tt>  <tt class="py-line">                <tt class="py-keyword">for</tt> <tt class="py-name">t</tt> <tt class="py-keyword">in</tt> <tt class="py-name">range</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">cd_steps</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
<a name="L236"></a><tt class="py-lineno">236</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-8" class="py-name" targets="Method deepbelief.abstractbm.AbstractBM.backward()=deepbelief.abstractbm.AbstractBM-class.html#backward,Method deepbelief.dbn.DBN.backward()=deepbelief.dbn.DBN-class.html#backward,Method deepbelief.gaussianrbm.GaussianRBM.backward()=deepbelief.gaussianrbm.GaussianRBM-class.html#backward,Method deepbelief.mixbm.MixBM.backward()=deepbelief.mixbm.MixBM-class.html#backward,Method deepbelief.rbm.RBM.backward()=deepbelief.rbm.RBM-class.html#backward,Method deepbelief.semirbm.SemiRBM.backward()=deepbelief.semirbm.SemiRBM-class.html#backward"><a title="deepbelief.abstractbm.AbstractBM.backward
deepbelief.dbn.DBN.backward
deepbelief.gaussianrbm.GaussianRBM.backward
deepbelief.mixbm.MixBM.backward
deepbelief.rbm.RBM.backward
deepbelief.semirbm.SemiRBM.backward" class="py-name" href="#" onclick="return doclink('link-8', 'backward', 'link-8');">backward</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L237"></a><tt class="py-lineno">237</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-9" class="py-name"><a title="deepbelief.abstractbm.AbstractBM.forward
deepbelief.dbn.DBN.forward
deepbelief.gaussianrbm.GaussianRBM.forward
deepbelief.mixbm.MixBM.forward
deepbelief.rbm.RBM.forward
deepbelief.semirbm.SemiRBM.forward" class="py-name" href="#" onclick="return doclink('link-9', 'forward', 'link-7');">forward</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L238"></a><tt class="py-lineno">238</tt>  <tt class="py-line"> </tt>
<a name="L239"></a><tt class="py-lineno">239</tt>  <tt class="py-line">                <tt class="py-keyword">if</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">persistent</tt><tt class="py-op">:</tt> </tt>
<a name="L240"></a><tt class="py-lineno">240</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pX</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">copy</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L241"></a><tt class="py-lineno">241</tt>  <tt class="py-line">                        <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">pY</tt> <tt class="py-op">=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt><tt class="py-op">.</tt><tt class="py-name">copy</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L242"></a><tt class="py-lineno">242</tt>  <tt class="py-line"> </tt>
<a name="L243"></a><tt class="py-lineno">243</tt>  <tt class="py-line">                <tt class="py-comment"># update parameters</tt> </tt>
<a name="L244"></a><tt class="py-lineno">244</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dW</tt> <tt class="py-op">=</tt> <tt class="py-name">X</tt> <tt class="py-op">*</tt> <tt class="py-name">Q</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> <tt class="py-op">/</tt> <tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">shape</tt><tt class="py-op">[</tt><tt class="py-number">1</tt><tt class="py-op">]</tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Q</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> <tt class="py-op">/</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">shape</tt><tt class="py-op">[</tt><tt class="py-number">1</tt><tt class="py-op">]</tt> \ </tt>
<a name="L245"></a><tt class="py-lineno">245</tt>  <tt class="py-line">                        <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">weight_decay</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">W</tt> \ </tt>
<a name="L246"></a><tt class="py-lineno">246</tt>  <tt class="py-line">                        <tt class="py-op">+</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">momentum</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dW</tt> </tt>
<a name="L247"></a><tt class="py-lineno">247</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">db</tt> <tt class="py-op">=</tt> <tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">mean</tt><tt class="py-op">(</tt><tt class="py-number">1</tt><tt class="py-op">)</tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">mean</tt><tt class="py-op">(</tt><tt class="py-number">1</tt><tt class="py-op">)</tt> <tt class="py-op">+</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">momentum</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">db</tt> </tt>
<a name="L248"></a><tt class="py-lineno">248</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dc</tt> <tt class="py-op">=</tt> <tt class="py-name">Q</tt><tt class="py-op">.</tt><tt class="py-name">mean</tt><tt class="py-op">(</tt><tt class="py-number">1</tt><tt class="py-op">)</tt> <tt class="py-op">-</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Q</tt><tt class="py-op">.</tt><tt class="py-name">mean</tt><tt class="py-op">(</tt><tt class="py-number">1</tt><tt class="py-op">)</tt> <tt class="py-op">+</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">momentum</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dc</tt> </tt>
<a name="L249"></a><tt class="py-lineno">249</tt>  <tt class="py-line"><tt class="py-comment">#                       - self.sparseness * np.multiply(np.multiply(Q, 1. - Q).mean(1), (Q.mean(1) - self.sparseness_target))</tt> </tt>
<a name="L250"></a><tt class="py-lineno">250</tt>  <tt class="py-line"> </tt>
<a name="L251"></a><tt class="py-lineno">251</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">W</tt> <tt class="py-op">+=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dW</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">learning_rate</tt> </tt>
<a name="L252"></a><tt class="py-lineno">252</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">b</tt> <tt class="py-op">+=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">db</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">learning_rate</tt> </tt>
<a name="L253"></a><tt class="py-lineno">253</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">c</tt> <tt class="py-op">+=</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dc</tt> <tt class="py-op">*</tt> <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">learning_rate</tt> </tt>
</div><a name="L254"></a><tt class="py-lineno">254</tt>  <tt class="py-line"> </tt>
<a name="L255"></a><tt class="py-lineno">255</tt>  <tt class="py-line"> </tt>
<a name="L256"></a><tt class="py-lineno">256</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.estimate_log_partition_function"></a><div id="AbstractBM.estimate_log_partition_function-def"><a name="L257"></a><tt class="py-lineno">257</tt> <a class="py-toggle" href="#" id="AbstractBM.estimate_log_partition_function-toggle" onclick="return toggle('AbstractBM.estimate_log_partition_function');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#estimate_log_partition_function">estimate_log_partition_function</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">num_ais_samples</tt><tt class="py-op">=</tt><tt class="py-number">100</tt><tt class="py-op">,</tt> <tt class="py-param">beta_weights</tt><tt class="py-op">=</tt><tt class="py-op">[</tt><tt class="py-op">]</tt><tt class="py-op">,</tt> <tt class="py-param">layer</tt><tt class="py-op">=</tt><tt class="py-op">-</tt><tt class="py-number">1</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.estimate_log_partition_function-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.estimate_log_partition_function-expanded"><a name="L258"></a><tt class="py-lineno">258</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L259"></a><tt class="py-lineno">259</tt>  <tt class="py-line"><tt class="py-docstring">                Estimate the logarithm of the partition function using annealed importance sampling.</tt> </tt>
<a name="L260"></a><tt class="py-lineno">260</tt>  <tt class="py-line"><tt class="py-docstring">                This method is a wrapper for the L{Estimator} class and is provided for convenience.</tt> </tt>
<a name="L261"></a><tt class="py-lineno">261</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L262"></a><tt class="py-lineno">262</tt>  <tt class="py-line"><tt class="py-docstring">                @type  num_ais_samples: integer</tt> </tt>
<a name="L263"></a><tt class="py-lineno">263</tt>  <tt class="py-line"><tt class="py-docstring">                @param num_ais_samples: number of samples used to estimate the partition function</tt> </tt>
<a name="L264"></a><tt class="py-lineno">264</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L265"></a><tt class="py-lineno">265</tt>  <tt class="py-line"><tt class="py-docstring">                @type  beta_weights: list</tt> </tt>
<a name="L266"></a><tt class="py-lineno">266</tt>  <tt class="py-line"><tt class="py-docstring">                @param beta_weights: annealing weights ranging from zero to one</tt> </tt>
<a name="L267"></a><tt class="py-lineno">267</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L268"></a><tt class="py-lineno">268</tt>  <tt class="py-line"><tt class="py-docstring">                @rtype:  real</tt> </tt>
<a name="L269"></a><tt class="py-lineno">269</tt>  <tt class="py-line"><tt class="py-docstring">                @return: log of the estimated partition function</tt> </tt>
<a name="L270"></a><tt class="py-lineno">270</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L271"></a><tt class="py-lineno">271</tt>  <tt class="py-line"> </tt>
<a name="L272"></a><tt class="py-lineno">272</tt>  <tt class="py-line">                <tt class="py-keyword">import</tt> <tt id="link-10" class="py-name" targets="Module deepbelief.estimator=deepbelief.estimator-module.html"><a title="deepbelief.estimator" class="py-name" href="#" onclick="return doclink('link-10', 'estimator', 'link-10');">estimator</a></tt> </tt>
<a name="L273"></a><tt class="py-lineno">273</tt>  <tt class="py-line">                <tt class="py-keyword">return</tt> <tt id="link-11" class="py-name"><a title="deepbelief.estimator" class="py-name" href="#" onclick="return doclink('link-11', 'estimator', 'link-10');">estimator</a></tt><tt class="py-op">.</tt><tt id="link-12" class="py-name" targets="Class deepbelief.estimator.Estimator=deepbelief.estimator.Estimator-class.html"><a title="deepbelief.estimator.Estimator" class="py-name" href="#" onclick="return doclink('link-12', 'Estimator', 'link-12');">Estimator</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt id="link-13" class="py-name" targets="Method deepbelief.abstractbm.AbstractBM.estimate_log_partition_function()=deepbelief.abstractbm.AbstractBM-class.html#estimate_log_partition_function,Method deepbelief.dbn.DBN.estimate_log_partition_function()=deepbelief.dbn.DBN-class.html#estimate_log_partition_function,Method deepbelief.estimator.Estimator.estimate_log_partition_function()=deepbelief.estimator.Estimator-class.html#estimate_log_partition_function"><a title="deepbelief.abstractbm.AbstractBM.estimate_log_partition_function
deepbelief.dbn.DBN.estimate_log_partition_function
deepbelief.estimator.Estimator.estimate_log_partition_function" class="py-name" href="#" onclick="return doclink('link-13', 'estimate_log_partition_function', 'link-13');">estimate_log_partition_function</a></tt><tt class="py-op">(</tt><tt class="py-name">num_ais_samples</tt><tt class="py-op">,</tt> <tt class="py-name">beta_weights</tt><tt class="py-op">,</tt> <tt class="py-name">layer</tt><tt class="py-op">)</tt> </tt>
</div><a name="L274"></a><tt class="py-lineno">274</tt>  <tt class="py-line"> </tt>
<a name="L275"></a><tt class="py-lineno">275</tt>  <tt class="py-line"> </tt>
<a name="L276"></a><tt class="py-lineno">276</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.estimate_log_likelihood"></a><div id="AbstractBM.estimate_log_likelihood-def"><a name="L277"></a><tt class="py-lineno">277</tt> <a class="py-toggle" href="#" id="AbstractBM.estimate_log_likelihood-toggle" onclick="return toggle('AbstractBM.estimate_log_likelihood');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#estimate_log_likelihood">estimate_log_likelihood</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.estimate_log_likelihood-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.estimate_log_likelihood-expanded"><a name="L278"></a><tt class="py-lineno">278</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L279"></a><tt class="py-lineno">279</tt>  <tt class="py-line"><tt class="py-docstring">                Estimate the log-likelihood of the model with respect to a set of data samples.</tt> </tt>
<a name="L280"></a><tt class="py-lineno">280</tt>  <tt class="py-line"><tt class="py-docstring">                This method uses the L{Estimator} class.</tt> </tt>
<a name="L281"></a><tt class="py-lineno">281</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L282"></a><tt class="py-lineno">282</tt>  <tt class="py-line"><tt class="py-docstring">                @type  X: array_like</tt> </tt>
<a name="L283"></a><tt class="py-lineno">283</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: data points</tt> </tt>
<a name="L284"></a><tt class="py-lineno">284</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L285"></a><tt class="py-lineno">285</tt>  <tt class="py-line"><tt class="py-docstring">                @rtype:  real</tt> </tt>
<a name="L286"></a><tt class="py-lineno">286</tt>  <tt class="py-line"><tt class="py-docstring">                @return: the average model log-likelihood in nats</tt> </tt>
<a name="L287"></a><tt class="py-lineno">287</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L288"></a><tt class="py-lineno">288</tt>  <tt class="py-line"> </tt>
<a name="L289"></a><tt class="py-lineno">289</tt>  <tt class="py-line">                <tt class="py-keyword">import</tt> <tt id="link-14" class="py-name"><a title="deepbelief.estimator" class="py-name" href="#" onclick="return doclink('link-14', 'estimator', 'link-10');">estimator</a></tt> </tt>
<a name="L290"></a><tt class="py-lineno">290</tt>  <tt class="py-line">                <tt class="py-keyword">return</tt> <tt id="link-15" class="py-name"><a title="deepbelief.estimator" class="py-name" href="#" onclick="return doclink('link-15', 'estimator', 'link-10');">estimator</a></tt><tt class="py-op">.</tt><tt id="link-16" class="py-name"><a title="deepbelief.estimator.Estimator" class="py-name" href="#" onclick="return doclink('link-16', 'Estimator', 'link-12');">Estimator</a></tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt id="link-17" class="py-name" targets="Method deepbelief.estimator.Estimator.estimate_log_probability()=deepbelief.estimator.Estimator-class.html#estimate_log_probability"><a title="deepbelief.estimator.Estimator.estimate_log_probability" class="py-name" href="#" onclick="return doclink('link-17', 'estimate_log_probability', 'link-17');">estimate_log_probability</a></tt><tt class="py-op">(</tt><tt class="py-name">X</tt><tt class="py-op">)</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">.</tt><tt class="py-name">mean</tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
</div><a name="L291"></a><tt class="py-lineno">291</tt>  <tt class="py-line"> </tt>
<a name="L292"></a><tt class="py-lineno">292</tt>  <tt class="py-line"> </tt>
<a name="L293"></a><tt class="py-lineno">293</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._sample_gibbs_step"></a><div id="AbstractBM._sample_gibbs_step-def"><a name="L294"></a><tt class="py-lineno">294</tt> <a class="py-toggle" href="#" id="AbstractBM._sample_gibbs_step-toggle" onclick="return toggle('AbstractBM._sample_gibbs_step');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_sample_gibbs_step">_sample_gibbs_step</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._sample_gibbs_step-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._sample_gibbs_step-expanded"><a name="L295"></a><tt class="py-lineno">295</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L296"></a><tt class="py-lineno">296</tt>  <tt class="py-line"><tt class="py-docstring">                Performs one step of Gibbs sampling.</tt> </tt>
<a name="L297"></a><tt class="py-lineno">297</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L298"></a><tt class="py-lineno">298</tt>  <tt class="py-line"> </tt>
<a name="L299"></a><tt class="py-lineno">299</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-18" class="py-name"><a title="deepbelief.abstractbm.AbstractBM.forward
deepbelief.dbn.DBN.forward
deepbelief.gaussianrbm.GaussianRBM.forward
deepbelief.mixbm.MixBM.forward
deepbelief.rbm.RBM.forward
deepbelief.semirbm.SemiRBM.forward" class="py-name" href="#" onclick="return doclink('link-18', 'forward', 'link-7');">forward</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
<a name="L300"></a><tt class="py-lineno">300</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt id="link-19" class="py-name"><a title="deepbelief.abstractbm.AbstractBM.backward
deepbelief.dbn.DBN.backward
deepbelief.gaussianrbm.GaussianRBM.backward
deepbelief.mixbm.MixBM.backward
deepbelief.rbm.RBM.backward
deepbelief.semirbm.SemiRBM.backward" class="py-name" href="#" onclick="return doclink('link-19', 'backward', 'link-8');">backward</a></tt><tt class="py-op">(</tt><tt class="py-op">)</tt> </tt>
</div><a name="L301"></a><tt class="py-lineno">301</tt>  <tt class="py-line"> </tt>
<a name="L302"></a><tt class="py-lineno">302</tt>  <tt class="py-line"> </tt>
<a name="L303"></a><tt class="py-lineno">303</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._train_sleep"></a><div id="AbstractBM._train_sleep-def"><a name="L304"></a><tt class="py-lineno">304</tt> <a class="py-toggle" href="#" id="AbstractBM._train_sleep-toggle" onclick="return toggle('AbstractBM._train_sleep');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_train_sleep">_train_sleep</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._train_sleep-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._train_sleep-expanded"><a name="L305"></a><tt class="py-lineno">305</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L306"></a><tt class="py-lineno">306</tt>  <tt class="py-line"><tt class="py-docstring">                Optimize conditinal likelihood for Y given X.</tt> </tt>
<a name="L307"></a><tt class="py-lineno">307</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L308"></a><tt class="py-lineno">308</tt>  <tt class="py-line"><tt class="py-docstring">                @type  X: array_like</tt> </tt>
<a name="L309"></a><tt class="py-lineno">309</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: visible states stored in columns</tt> </tt>
<a name="L310"></a><tt class="py-lineno">310</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L311"></a><tt class="py-lineno">311</tt>  <tt class="py-line"><tt class="py-docstring">                @type  Y: array_like</tt> </tt>
<a name="L312"></a><tt class="py-lineno">312</tt>  <tt class="py-line"><tt class="py-docstring">                @param Y: hidden states stored in columns</tt> </tt>
<a name="L313"></a><tt class="py-lineno">313</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L314"></a><tt class="py-lineno">314</tt>  <tt class="py-line"> </tt>
<a name="L315"></a><tt class="py-lineno">315</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_train_sleep\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L316"></a><tt class="py-lineno">316</tt>  <tt class="py-line"> </tt>
<a name="L317"></a><tt class="py-lineno">317</tt>  <tt class="py-line"> </tt>
<a name="L318"></a><tt class="py-lineno">318</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._train_wake"></a><div id="AbstractBM._train_wake-def"><a name="L319"></a><tt class="py-lineno">319</tt> <a class="py-toggle" href="#" id="AbstractBM._train_wake-toggle" onclick="return toggle('AbstractBM._train_wake');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_train_wake">_train_wake</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._train_wake-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._train_wake-expanded"><a name="L320"></a><tt class="py-lineno">320</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L321"></a><tt class="py-lineno">321</tt>  <tt class="py-line"><tt class="py-docstring">                Optimize conditinal likelihood for X given Y.</tt> </tt>
<a name="L322"></a><tt class="py-lineno">322</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L323"></a><tt class="py-lineno">323</tt>  <tt class="py-line"><tt class="py-docstring">                @type  X: array_like</tt> </tt>
<a name="L324"></a><tt class="py-lineno">324</tt>  <tt class="py-line"><tt class="py-docstring">                @param X: visible states stored in columns</tt> </tt>
<a name="L325"></a><tt class="py-lineno">325</tt>  <tt class="py-line"><tt class="py-docstring"></tt> </tt>
<a name="L326"></a><tt class="py-lineno">326</tt>  <tt class="py-line"><tt class="py-docstring">                @type  Y: array_like</tt> </tt>
<a name="L327"></a><tt class="py-lineno">327</tt>  <tt class="py-line"><tt class="py-docstring">                @param Y: hidden states stored in columns</tt> </tt>
<a name="L328"></a><tt class="py-lineno">328</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L329"></a><tt class="py-lineno">329</tt>  <tt class="py-line"> </tt>
<a name="L330"></a><tt class="py-lineno">330</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_train_wake\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L331"></a><tt class="py-lineno">331</tt>  <tt class="py-line"> </tt>
<a name="L332"></a><tt class="py-lineno">332</tt>  <tt class="py-line"> </tt>
<a name="L333"></a><tt class="py-lineno">333</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM.clear"></a><div id="AbstractBM.clear-def"><a name="L334"></a><tt class="py-lineno">334</tt> <a class="py-toggle" href="#" id="AbstractBM.clear-toggle" onclick="return toggle('AbstractBM.clear');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#clear">clear</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM.clear-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM.clear-expanded"><a name="L335"></a><tt class="py-lineno">335</tt>  <tt class="py-line">                <tt class="py-docstring">"""</tt> </tt>
<a name="L336"></a><tt class="py-lineno">336</tt>  <tt class="py-line"><tt class="py-docstring">                Reset variables. This method can help to free memory.</tt> </tt>
<a name="L337"></a><tt class="py-lineno">337</tt>  <tt class="py-line"><tt class="py-docstring">                """</tt> </tt>
<a name="L338"></a><tt class="py-lineno">338</tt>  <tt class="py-line"> </tt>
<a name="L339"></a><tt class="py-lineno">339</tt>  <tt class="py-line">                <tt class="py-comment"># variables</tt> </tt>
<a name="L340"></a><tt class="py-lineno">340</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">.</tt><tt class="py-name">shape</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> </tt>
<a name="L341"></a><tt class="py-lineno">341</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">asmatrix</tt><tt class="py-op">(</tt><tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt><tt class="py-op">.</tt><tt class="py-name">shape</tt><tt class="py-op">[</tt><tt class="py-number">0</tt><tt class="py-op">]</tt><tt class="py-op">)</tt><tt class="py-op">)</tt><tt class="py-op">.</tt><tt class="py-name">T</tt> </tt>
<a name="L342"></a><tt class="py-lineno">342</tt>  <tt class="py-line"> </tt>
<a name="L343"></a><tt class="py-lineno">343</tt>  <tt class="py-line">                <tt class="py-comment"># increments</tt> </tt>
<a name="L344"></a><tt class="py-lineno">344</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dW</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">W</tt><tt class="py-op">)</tt> </tt>
<a name="L345"></a><tt class="py-lineno">345</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">db</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">b</tt><tt class="py-op">)</tt> </tt>
<a name="L346"></a><tt class="py-lineno">346</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">dc</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">c</tt><tt class="py-op">)</tt> </tt>
<a name="L347"></a><tt class="py-lineno">347</tt>  <tt class="py-line"> </tt>
<a name="L348"></a><tt class="py-lineno">348</tt>  <tt class="py-line">                <tt class="py-comment"># probabilities</tt> </tt>
<a name="L349"></a><tt class="py-lineno">349</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">P</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">X</tt><tt class="py-op">)</tt> </tt>
<a name="L350"></a><tt class="py-lineno">350</tt>  <tt class="py-line">                <tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Q</tt> <tt class="py-op">=</tt> <tt class="py-name">np</tt><tt class="py-op">.</tt><tt class="py-name">zeros_like</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">Y</tt><tt class="py-op">)</tt> </tt>
</div><a name="L351"></a><tt class="py-lineno">351</tt>  <tt class="py-line"> </tt>
<a name="L352"></a><tt class="py-lineno">352</tt>  <tt class="py-line"> </tt>
<a name="L353"></a><tt class="py-lineno">353</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._free_energy"></a><div id="AbstractBM._free_energy-def"><a name="L354"></a><tt class="py-lineno">354</tt> <a class="py-toggle" href="#" id="AbstractBM._free_energy-toggle" onclick="return toggle('AbstractBM._free_energy');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_free_energy">_free_energy</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._free_energy-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._free_energy-expanded"><a name="L355"></a><tt class="py-lineno">355</tt>  <tt class="py-line">                <tt class="py-keyword">return</tt> <tt class="py-op">-</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">_ulogprob_vis</tt><tt class="py-op">(</tt><tt class="py-name">X</tt><tt class="py-op">)</tt> </tt>
</div><a name="L356"></a><tt class="py-lineno">356</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._free_energy_gradient"></a><div id="AbstractBM._free_energy_gradient-def"><a name="L357"></a><tt class="py-lineno">357</tt> <a class="py-toggle" href="#" id="AbstractBM._free_energy_gradient-toggle" onclick="return toggle('AbstractBM._free_energy_gradient');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_free_energy_gradient">_free_energy_gradient</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._free_energy_gradient-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._free_energy_gradient-expanded"><a name="L358"></a><tt class="py-lineno">358</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_free_energy_gradient\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L359"></a><tt class="py-lineno">359</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._ulogprob"></a><div id="AbstractBM._ulogprob-def"><a name="L360"></a><tt class="py-lineno">360</tt> <a class="py-toggle" href="#" id="AbstractBM._ulogprob-toggle" onclick="return toggle('AbstractBM._ulogprob');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_ulogprob">_ulogprob</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">,</tt> <tt class="py-param">all_pairs</tt><tt class="py-op">=</tt><tt class="py-name">False</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._ulogprob-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._ulogprob-expanded"><a name="L361"></a><tt class="py-lineno">361</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_ulogprob\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L362"></a><tt class="py-lineno">362</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._ulogprob_vis"></a><div id="AbstractBM._ulogprob_vis-def"><a name="L363"></a><tt class="py-lineno">363</tt> <a class="py-toggle" href="#" id="AbstractBM._ulogprob_vis-toggle" onclick="return toggle('AbstractBM._ulogprob_vis');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_ulogprob_vis">_ulogprob_vis</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._ulogprob_vis-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._ulogprob_vis-expanded"><a name="L364"></a><tt class="py-lineno">364</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_ulogprob_vis\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L365"></a><tt class="py-lineno">365</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._ulogprob_hid"></a><div id="AbstractBM._ulogprob_hid-def"><a name="L366"></a><tt class="py-lineno">366</tt> <a class="py-toggle" href="#" id="AbstractBM._ulogprob_hid-toggle" onclick="return toggle('AbstractBM._ulogprob_hid');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_ulogprob_hid">_ulogprob_hid</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._ulogprob_hid-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._ulogprob_hid-expanded"><a name="L367"></a><tt class="py-lineno">367</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_ulogprob_hid\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L368"></a><tt class="py-lineno">368</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._clogprob_vis_hid"></a><div id="AbstractBM._clogprob_vis_hid-def"><a name="L369"></a><tt class="py-lineno">369</tt> <a class="py-toggle" href="#" id="AbstractBM._clogprob_vis_hid-toggle" onclick="return toggle('AbstractBM._clogprob_vis_hid');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_clogprob_vis_hid">_clogprob_vis_hid</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">,</tt> <tt class="py-param">all_pairs</tt><tt class="py-op">=</tt><tt class="py-name">False</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._clogprob_vis_hid-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._clogprob_vis_hid-expanded"><a name="L370"></a><tt class="py-lineno">370</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_clogprob_vis_hid\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L371"></a><tt class="py-lineno">371</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._clogprob_hid_vis"></a><div id="AbstractBM._clogprob_hid_vis-def"><a name="L372"></a><tt class="py-lineno">372</tt> <a class="py-toggle" href="#" id="AbstractBM._clogprob_hid_vis-toggle" onclick="return toggle('AbstractBM._clogprob_hid_vis');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_clogprob_hid_vis">_clogprob_hid_vis</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">,</tt> <tt class="py-param">Y</tt><tt class="py-op">,</tt> <tt class="py-param">all_pairs</tt><tt class="py-op">=</tt><tt class="py-name">False</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._clogprob_hid_vis-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._clogprob_hid_vis-expanded"><a name="L373"></a><tt class="py-lineno">373</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_clogprob_hid_vis\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div><a name="L374"></a><tt class="py-lineno">374</tt>  <tt class="py-line"> </tt>
<a name="AbstractBM._centropy_hid_vis"></a><div id="AbstractBM._centropy_hid_vis-def"><a name="L375"></a><tt class="py-lineno">375</tt> <a class="py-toggle" href="#" id="AbstractBM._centropy_hid_vis-toggle" onclick="return toggle('AbstractBM._centropy_hid_vis');">-</a><tt class="py-line">        <tt class="py-keyword">def</tt> <a class="py-def-name" href="deepbelief.abstractbm.AbstractBM-class.html#_centropy_hid_vis">_centropy_hid_vis</a><tt class="py-op">(</tt><tt class="py-param">self</tt><tt class="py-op">,</tt> <tt class="py-param">X</tt><tt class="py-op">)</tt><tt class="py-op">:</tt> </tt>
</div><div id="AbstractBM._centropy_hid_vis-collapsed" style="display:none;" pad="+++" indent="++++++++++++"></div><div id="AbstractBM._centropy_hid_vis-expanded"><a name="L376"></a><tt class="py-lineno">376</tt>  <tt class="py-line">                <tt class="py-keyword">raise</tt> <tt class="py-name">Exception</tt><tt class="py-op">(</tt><tt class="py-string">'Abstract method \'_centropy\' not implemented in '</tt> <tt class="py-op">+</tt> <tt class="py-name">str</tt><tt class="py-op">(</tt><tt class="py-name">self</tt><tt class="py-op">.</tt><tt class="py-name">__class__</tt><tt class="py-op">)</tt><tt class="py-op">)</tt> </tt>
</div></div><a name="L377"></a><tt class="py-lineno">377</tt>  <tt class="py-line"> </tt><script type="text/javascript">
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